DeepScaleR Description
DeepScaleR is a sophisticated language model comprising 1.5 billion parameters, refined from DeepSeek-R1-Distilled-Qwen-1.5B through the use of distributed reinforcement learning combined with an innovative strategy that incrementally expands its context window from 8,000 to 24,000 tokens during the training process. This model was developed using approximately 40,000 meticulously selected mathematical problems sourced from high-level competition datasets, including AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. Achieving an impressive 43.1% accuracy on the AIME 2024 exam, DeepScaleR demonstrates a significant enhancement of around 14.3 percentage points compared to its base model, and it even outperforms the proprietary O1-Preview model, which is considerably larger. Additionally, it excels on a variety of mathematical benchmarks such as MATH-500, AMC 2023, Minerva Math, and OlympiadBench, indicating that smaller, optimized models fine-tuned with reinforcement learning can rival or surpass the capabilities of larger models in complex reasoning tasks. This advancement underscores the potential of efficient modeling approaches in the realm of mathematical problem-solving.
DeepScaleR Alternatives
PackageX OCR Scanning
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Our OCR API has been trained using information from more than 10 million labels. This allows for the highest scanning accuracy in the market, at over 95%.
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Create your own OCR scanner app to eliminate pen-and-paper inefficiencies.
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Detect and extract information from barcodes or QR codes.
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RunPod
RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.
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DeepCoder
DeepCoder, an entirely open-source model for code reasoning and generation, has been developed through a partnership between Agentica Project and Together AI. Leveraging the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has undergone fine-tuning via distributed reinforcement learning, achieving a notable accuracy of 60.6% on LiveCodeBench, which marks an 8% enhancement over its predecessor. This level of performance rivals that of proprietary models like o3-mini (2025-01-031 Low) and o1, all while operating with only 14 billion parameters. The training process spanned 2.5 weeks on 32 H100 GPUs, utilizing a carefully curated dataset of approximately 24,000 coding challenges sourced from validated platforms, including TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each problem mandated a legitimate solution along with a minimum of five unit tests to guarantee reliability during reinforcement learning training. Furthermore, to effectively manage long-range context, DeepCoder incorporates strategies such as iterative context lengthening and overlong filtering, ensuring it remains adept at handling complex coding tasks. This innovative approach allows DeepCoder to maintain high standards of accuracy and reliability in its code generation capabilities.
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Athene-V2
Nexusflow has unveiled Athene-V2, its newest model suite boasting 72 billion parameters, which has been meticulously fine-tuned from Qwen 2.5 72B to rival the capabilities of GPT-4o. Within this suite, Athene-V2-Chat-72B stands out as a cutting-edge chat model that performs comparably to GPT-4o across various benchmarks; it excels particularly in chat helpfulness (Arena-Hard), ranks second in the code completion category on bigcode-bench-hard, and demonstrates strong abilities in mathematics (MATH) and accurate long log extraction. Furthermore, Athene-V2-Agent-72B seamlessly integrates chat and agent features, delivering clear and directive responses while surpassing GPT-4o in Nexus-V2 function calling benchmarks, specifically tailored for intricate enterprise-level scenarios. These innovations highlight a significant industry transition from merely increasing model sizes to focusing on specialized customization, showcasing how targeted post-training techniques can effectively enhance models for specific skills and applications. As technology continues to evolve, it becomes essential for developers to leverage these advancements to create increasingly sophisticated AI solutions.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
No Integrations at this time
Company Details
Company:
Agentica Project
Year Founded:
2025
Headquarters:
United States
Website:
agentica-project.com
Media
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Product Details
Platforms
Windows
Mac
Linux
On-Premises
Types of Training
Training Docs
Customer Support
Online Support
DeepScaleR Features and Options
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